Project Summary/ Abstract Repetitive manual lifting is a significant occupational health and safety concern and is highly prevalent in warehousing, distribution centers, package delivery, transportation, and lean manufacturing. These types of tasks are the most challenging to analyze from an ergonomics perspective, particularly in multi-task situations where lifting varied items occurs in numerous locations, involving variable body postures throughout the workday. Manually measuring the parameters needed for analysis is challenging and resource intensive for industry practitioners today. The overarching goal of this research is to create a computer vision risk model for lifting, incorporate it into a prototype instrument, and field evaluate the instrument in comparison to conventional RNLE methods. Automated job analysis potentially offers a more objective, accurate, repeatable, and efficient exposure assessment tool than conventional observational methods. Furthermore, it provides convenient quantification of additional exposure variables, including lifting kinematics (i.e., speed and acceleration) individual differences, and postures; is suitable for long-term, direct reading exposure assessment; and offers animated data visualization synchronized with video for identifying interventions. This research translates already collected videos of jobs and corresponding health outcomes from a landmark prospective study database for computer vision lower back pain risk assessment. It leverages the vast database of videos and corresponding exposure measures and health data for lifting and lowering activities (i.e., subtasks) performed by 772 workers across the three cohort studies, collected by our study partners at NIOSH, the University of Utah, and the University of Wisconsin-Milwaukee. They are part of a multi-institutional NIOSH funded consortium of U.S. laboratories that recently studied workers in a wide variety of industries in a prospective epidemiology study on lower back pain. The consortium videos will be analyzed by extracting the new video feature exposure measures, including lifting postures, and torso and load kinematics. The video exposure assessment data will be combined with consortium observational exposure measures and health outcome data. We will test the hypothesis that adding computer vision exposure variables with consortium exposure variables can enhance performance of predicting lower back pain. This project will refine and program video exposure assessment algorithms for posture classification, torso angle and trunk and load kinematics into a prototype device. The new exposure algorithms will be tested in selected industrial sites and compared against conventional observational methods for consistency and utility (r2p). This translational research offers an unprecedented opportunity to exploit unique videos and associated exposure and health outcome data already collected, in combination with new technology for quantifying exp...